STTR Phase I: Automated Music Theory Instruction for Secondary and Post-Secondary Education

STTR 第一阶段：中等和高等教育的自动化音乐理论教学

• 批准号: 1448915
• 负责人: Halim Beere
• 金额: $ 22.5万
• 依托单位: Illiac Software, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1448915&HistoricalAwards=false
• 关键词: STTR; Phase; Automated; Music; Theory

This STTR Phase I project develops automated instruction for the music education market, replacing human labor with music analysis algorithms, and paper course materials with digital multimedia documents. Despite the positive effects of art education on achievement, many arts programs are being cut or underfunded; as a result these students are at higher risk of poorer grades and not completing their college degrees. Learning music requires extensive practice and individualized guidance, as taught in private lessons for hundreds of years. The proposed solution brings a unique and innovative software system to the marketplace that will automate and reinforce substantial parts of music theory instruction. Early results indicate that this technology can improve learning thereby reducing the need for remediation at higher grade levels and helping maintain the benefits of arts education in US schools even in the face of budgetary pressures. Numerous studies have demonstrated that increased emphasis on education in the arts stimulates intellectual capacity and creates wider benefits such as facility in STEM (Science, Technology, Engineering, and Mathematics) and interpersonal skills. This is a market of millions of students with extensions into larger markets, such as amateur musicians throughout the world.The key innovation of the software lies in its proprietary music analysis algorithms that enable substantial amounts of music theory instruction to be automated, including close to real-time correction, feedback and grading of complex, secondary and post-secondary exercises in tonal music analysis and composition. By using the software in place of paper instruction materials, a music student can practice composition and analysis as much as needed to master topics, receiving instantaneous feedback and guidance from the digital teacher. For music instructors, the technology completely removes the need to grade papers, reducing what now takes them hours of human labor per week to seconds at the push of a button. The software implements a multimedia document model that integrates all the common types of media used in music theory pedagogy: music notation (with or without automatic analysis), MIDI (Musical Instrument Digital Interface), text editing, images, vector graphics, video and audio playback. Early results indicate that this technology improves learning, thereby reducing the need for remediation at higher grade levels and helping maintain the benefits of arts education in US schools even in the face of budgetary pressures.

STTR第一阶段项目为音乐教育市场开发自动化教学，用音乐分析算法取代人工劳动，用数字多媒体文档取代纸质课程材料。尽管艺术教育对成绩有积极影响，但许多艺术项目正在被削减或资金不足;因此，这些学生成绩较差，无法完成大学学位的风险更高。学习音乐需要广泛的实践和个性化的指导，数百年来一直在私人课程中教授。所提出的解决方案为市场带来了一个独特而创新的软件系统，该系统将自动化并加强音乐理论教学的重要部分。早期的结果表明，这项技术可以改善学习，从而减少对高年级补习的需求，并有助于保持美国学校艺术教育的好处，即使在面临预算压力的情况下。许多研究表明，对艺术教育的日益重视刺激了智力能力，并创造了更广泛的好处，如STEM（科学，技术，工程和数学）和人际交往技能的设施。这是一个拥有数百万学生的市场，并延伸到更大的市场，如世界各地的业余音乐家。该软件的关键创新在于其专有的音乐分析算法，使大量的音乐理论教学自动化，包括接近实时的校正，反馈和评分复杂的，中学和中学后的练习音调音乐分析和作曲。通过使用该软件代替纸质教学材料，音乐学生可以根据需要练习作曲和分析，以掌握主题，并从数字教师那里获得即时反馈和指导。对于音乐教师来说，这项技术完全消除了对论文评分的需要，将现在每周花费数小时的人力劳动减少到只需按下按钮即可完成。 该软件实现了一个多媒体文档模型，集成了音乐理论教学中使用的所有常见类型的媒体：乐谱（带或不带自动分析），乐器数字接口（MIDI），文本编辑，图像，矢量图形，视频和音频播放。早期的结果表明，这项技术改善了学习，从而减少了对高年级补习的需求，并有助于保持美国学校艺术教育的好处，即使在面临预算压力的情况下。